As we continue to improve our understanding of brain function, we are designing ever more
complicated neuroimaging paradigms to probe network behaviour and activation differences
between cohorts of individuals, particularly involving resting state data. However, we are
simultaneously becoming more aware of how difficult it is to distinguish between the neuronal
signal of interest and variance due to confounds such as gross or subtle head motion, respiratory
and cardiac variation, arousal levels, and other physiological sources. Papers demonstrating that
unmodeled noise confounds can bias results have raised alarm across the entire neuroimaging
community. Discussions of the influence of residual noise artifacts on study results are
increasingly common in the literature. New methods for characterizing and removing noise
signals from fMRI data have exploded in complexity and uptake over the last few years, reflected
by a recent special issue of NeuroImage edited by the course organizers and featuring articles by
the course presenters. Researchers are now keenly aware that noise can be a huge and tricky
problem in their data analysis and interpretation, but still commonly ask: “which noise correction
methods should I be using?” This course builds upon the previous pre-processing courses
presented at OHBM by tackling advanced noise removal techniques, providing researchers with
the practical tools and breadth of understanding to select the best approach for navigating noise
in their own fMRI data.